ArticlePloS one2013
t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.Frontiers in genetics · 2023Article
- Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.Biology · 2022Article
- Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model.BMC bioinformatics · 2021Article
- Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture.Molecular therapy. Nucleic acids · 2021Article
- Semi-supervised prediction of protein interaction sites from unlabeled sample information.BMC bioinformatics · 2019Article
- Research on predicting 2D-HP protein folding using reinforcement learning with full state space.BMC bioinformatics · 2019Article
- Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.Scientific reports · 2019Article
- A Network-guided Association Mapping Approach from DNA Methylation to Disease.Scientific reports · 2019Article
- Recurrent Neural Network for Predicting Transcription Factor Binding Sites.Scientific reports · 2018Article
- Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.Scientific reports · 2018Article
- PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.International journal of molecular sciences · 2018Article
- Fast sequence analysis based on diamond sampling.PloS one · 2018Article
- Features of reactive cysteines discovered through computation: from kinase inhibition to enrichment around protein degrons.Scientific reports · 2017Article
- Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network.Oncotarget · 2017Article
- Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.PloS one · 2017Article
- Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information.Oncotarget · 2016Article
- Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.BioMed research international · 2016Article
- Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.International journal of molecular sciences · 2015Article
- Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set.BMC bioinformatics · 2014Article
- Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.BioMed research international · 2014Article
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Authors and funding
4 authors.
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Abstract
Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.